What is SAS Data Quality?
SAS software for data preparation and data quality enables data transformation, self-service data integration and data enrichment for clean, reliable data. SAS Data Quality helps you make data-driven decisions you can trust. Use it to improve and monitor the health and value of your data so you can confidently fuel operations, compliance and analytics initiatives.
Company Details
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Real user data aggregated to summarize the product performance and customer experience.
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Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
89 Likeliness to Recommend
98 Plan to Renew
85 Satisfaction of Cost Relative to Value
Emotional Footprint Overview
Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.
+95 Net Emotional Footprint
The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.
How much do users love SAS Data Quality?
Pros
- Helps Innovate
- Performance Enhancing
- Trustworthy
- Efficient Service
How to read the Emotional Footprint
The Net Emotional Footprint measures high-level user sentiment towards particular product offerings. It aggregates emotional response ratings for various dimensions of the vendor-client relationship and product effectiveness, creating a powerful indicator of overall user feeling toward the vendor and product.
While purchasing decisions shouldn't be based on emotion, it's valuable to know what kind of emotional response the vendor you're considering elicits from their users.
Footprint
Negative
Neutral
Positive
Feature Ratings
Data Cleansing
Dashboard
Data Enrichment
Data Matching
Geocoding
Record Management
Reporting Components
Data Profiling
Data Monitoring and Administration
Data Source Connectivity
Record Deduplication
Vendor Capability Ratings
Ease of Implementation
Quality of Features
Breadth of Features
Ease of IT Administration
Availability and Quality of Training
Ease of Data Integration
Business Value Created
Product Strategy and Rate of Improvement
Ease of Customization
Usability and Intuitiveness
Vendor Support
SAS Data Quality Reviews
Akash A.
- Role: Human Resources
- Industry: Technology
- Involvement: Business Leader or Manager
Submitted Apr 2024
Superb idea
Likeliness to Recommend
What differentiates SAS Data Quality from other similar products?
Connect with dataset directly
What is your favorite aspect of this product?
Accessible completed
What do you dislike most about this product?
Complex filtration
What recommendations would you give to someone considering this product?
Unique features
Pros
- Helps Innovate
- Enables Productivity
- Trustworthy
- Effective Service
Peter R.
- Role: Information Technology
- Industry: Energy
- Involvement: End User of Application
Submitted Sep 2025
Data Cleansing & Standardization Platform.
Likeliness to Recommend
What differentiates SAS Data Quality from other similar products?
De-duplicates and links records across datasets using advanced matching algorithms. Helps analysts work with a unified customer view.
What is your favorite aspect of this product?
Works with large enterprise datasets, not just spreadsheets.
What do you dislike most about this product?
Complex interface and rules engine compared to lighter tools
What recommendations would you give to someone considering this product?
SAS Data Quality has a learning curve; budget for training or hire an experienced SAS consultant.
Pros
- Continually Improving Product
- Enables Productivity
- Efficient Service
- Inspires Innovation
Sourabh P.
- Role: Information Technology
- Industry: Technology
- Involvement: IT Leader or Manager
Submitted Jul 2025
Robust and Reliable, But Needs Modernization.
Likeliness to Recommend
What differentiates SAS Data Quality from other similar products?
SAS Data Quality stands out for its strong analytical foundation and deep integration with data science workflows. Unlike many tools that focus only on cleansing and matching, SAS ties data quality directly into broader analytics and machine learning pipelines. Its data profiling, parsing, and enrichment capabilities are highly customizable and backed by SAS’s decades of statistical expertise. Another key differentiator is its support for complex industry-specific rules and governance, which makes it ideal for regulated sectors like healthcare, finance, and insurance. Overall, it’s a powerful platform for organizations that treat data quality
What is your favorite aspect of this product?
My favorite aspect of SAS Data Quality is its powerful data profiling and parsing capabilities. It gives you a deep, detailed understanding of your data right from the start, helping you quickly spot inconsistencies, duplicates, and formatting issues. The fact that you can customize rules and workflows to fit very specific business needs is a huge plus. It’s especially valuable in complex environments where data quality isn’t just about cleanup—it’s about enabling accurate analytics and decision-making.
What do you dislike most about this product?
The biggest drawback of SAS Data Quality is its steep learning curve and complex interface. While it’s incredibly powerful, it’s not very beginner-friendly—especially for business users or those without a technical background. Setting up workflows or custom rules can feel overwhelming without proper training. Additionally, integration with modern cloud-native tools and platforms can require extra effort, which might slow down adoption in more agile or hybrid environments. A more intuitive UI and better out-of-the-box cloud connectivity would make a big difference.
What recommendations would you give to someone considering this product?
Ideal for Data-Driven Organizations: If your company heavily relies on analytics, forecasting, or regulatory reporting, SAS Data Quality is a great fit due to its deep profiling and rule-based cleansing. Be Ready for Training: The tool is powerful but not very intuitive at first. Invest in proper training or have experienced SAS professionals on your team to get the most out of it. Leverage Its Strengths: Use SAS's strong parsing, standardization, and matching tools, especially if your data spans multiple sources or formats. It’s particularly effective in industries with complex data structures.
Pros
- Continually Improving Product
- Reliable
- Performance Enhancing
- Enables Productivity
